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The goal of gtexr is to provide a convenient R interface to the GTEx Portal API V2.
New to R? Try out the ⭐shiny app⭐.
You can install this package from CRAN:
install.packages("gtexr")Or you can install the development version of gtexr from GitHub with:
# install.packages("devtools")
devtools::install_github("rmgpanw/gtexr")Get general information about the GTEx service:
library(gtexr)
get_service_info()
#> # A tibble: 1 × 9
#> id name version organization_name organization_url description contactUrl
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 org.g… GTEx… 2.0.0 GTEx Project https://gtexpor… This servi… https://g…
#> # ℹ 2 more variables: documentationUrl <chr>, environment <chr>Retrieve eQTL genes for whole blood tissue:
get_eqtl_genes("Whole_Blood")
#> Warning: ! Total number of items (12360) exceeds maximum page size (250).
#> ℹ Try increasing `itemsPerPage`.
#>
#> ── Paging info ─────────────────────────────────────────────────────────────────
#> • numberOfPages = 50
#> • page = 0
#> • maxItemsPerPage = 250
#> • totalNumberOfItems = 12360
#> # A tibble: 250 × 10
#> tissueSiteDetailId ontologyId datasetId empiricalPValue gencodeId geneSymbol
#> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 Whole_Blood UBERON:001… gtex_v8 1.05e- 9 ENSG0000… WASH7P
#> 2 Whole_Blood UBERON:001… gtex_v8 1.06e-25 ENSG0000… RP11-34P1…
#> 3 Whole_Blood UBERON:001… gtex_v8 6.31e- 2 ENSG0000… CICP27
#> 4 Whole_Blood UBERON:001… gtex_v8 8.71e- 9 ENSG0000… RP11-34P1…
#> 5 Whole_Blood UBERON:001… gtex_v8 6.01e-20 ENSG0000… RP11-34P1…
#> 6 Whole_Blood UBERON:001… gtex_v8 6.96e- 9 ENSG0000… RP11-34P1…
#> 7 Whole_Blood UBERON:001… gtex_v8 3.10e- 4 ENSG0000… RP11-34P1…
#> 8 Whole_Blood UBERON:001… gtex_v8 1.92e- 3 ENSG0000… ABC7-4304…
#> 9 Whole_Blood UBERON:001… gtex_v8 1.58e- 3 ENSG0000… RP11-34P1…
#> 10 Whole_Blood UBERON:001… gtex_v8 7.82e- 2 ENSG0000… AP006222.2
#> # ℹ 240 more rows
#> # ℹ 4 more variables: log2AllelicFoldChange <dbl>, pValue <dbl>,
#> # pValueThreshold <dbl>, qValue <dbl>Retrieve significant eQTLs for one or more genes:
get_significant_single_tissue_eqtls(gencodeId = c("ENSG00000132693.12",
"ENSG00000203782.5"))
#>
#> ── Paging info ─────────────────────────────────────────────────────────────────
#> • numberOfPages = 1
#> • page = 0
#> • maxItemsPerPage = 250
#> • totalNumberOfItems = 249
#> # A tibble: 249 × 13
#> snpId pos snpIdUpper variantId geneSymbol pValue geneSymbolUpper
#> <chr> <int> <chr> <chr> <chr> <dbl> <chr>
#> 1 rs12128960 159343657 RS12128960 chr1_1593… CRP 8.52e-5 CRP
#> 2 rs12132451 159344052 RS12132451 chr1_1593… CRP 7.92e-5 CRP
#> 3 rs12136402 159347493 RS12136402 chr1_1593… CRP 7.92e-5 CRP
#> 4 rs10908709 159350390 RS10908709 chr1_1593… CRP 7.92e-5 CRP
#> 5 rs10908710 159351189 RS10908710 chr1_1593… CRP 7.92e-5 CRP
#> 6 rs11265178 159359256 RS11265178 chr1_1593… CRP 9.62e-5 CRP
#> 7 rs35532309 159360755 RS35532309 chr1_1593… CRP 6.11e-5 CRP
#> 8 rs6692378 159369451 RS6692378 chr1_1593… CRP 1.17e-6 CRP
#> 9 rs10908714 159370563 RS10908714 chr1_1593… CRP 1.80e-5 CRP
#> 10 rs6656924 159372915 RS6656924 chr1_1593… CRP 1.00e-6 CRP
#> # ℹ 239 more rows
#> # ℹ 6 more variables: datasetId <chr>, tissueSiteDetailId <chr>,
#> # ontologyId <chr>, chromosome <chr>, gencodeId <chr>, nes <dbl>These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.